Evolutionary analysis of access control models: a formal concept analysis method
Bibliographic record
Abstract
Access control is an essential feature of most software systems security mechanisms. Role-Based Access Control (RBAC), likely the most popular access control technique, specifies user roles and associates each role with permissions to access distinct system data and functionalities. The types of system users, i.e., the roles, the sensitive system functionalities accessed through permissions, as well as the roles-permissions assignment rules evolve over time. In this paper, we discuss a methodology for analyzing and understanding the RBAC-evolution process and its relation to the overall evolutionary lifecycle of the system, motivated by the hypothesis that it may impact the overall system security. Our methodology relies on Formal Concept Analysis (FCA). First, we extract the roles-permissions matrix of each system version and we compute the implicit concept lattice. Next, we apply a suite of distance metrics to pairwise compare the matrices and concept lattices of subsequent system versions. By examining the evolution of these distance metrics, developers can easily notice which versions involve more, and more complex, RBAC changes that may indicate higher security risks. We demonstrate our methodology with a study of the MediaWiki platform.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".